Deep ROI-Based Modeling for Urban Human Mobility Prediction

Rapidly developing location acquisition technologies have provided us with big GPS trajectory data, which offers a new means of understanding people's daily behaviors as well as urban dynamics. With such data, predicting human mobility at the city level will be of great significance for transportation scheduling, urban regulation, and emergency management. In particular, most urban human behaviors are related to a small number of important regions, referred to as Regions-of-Interest (ROIs). Therefore, in this study, a deep ROI-based modeling approach is proposed for effectively predicting urban human mobility. Urban ROIs are first discovered from historical trajectory data, and urban human mobility is designated using two types of ROI labels (ISROI and WHICHROI). Then, urban mobility prediction is modeled as a sequence classification problem for each type of label. Finally, a deep-learning architecture built with recurrent neural networks is designed as an effective sequence classifier. Experimental results demonstrate that the superior performance of our proposed approach to the baseline models and several real-world practices show the applicability of our approach to real-world urban computing problems.